/*******************************************************************************
 *                       SimulatedRobotControl3laws.c                           *
 *                       -----------------------                               *
 * copyright            : (C) 2013 by Richard R. Carrillo and Niceto R. Luque  *
 * email                : rcarrillo, nluque at ugr.es                          *
 *******************************************************************************/

/***************************************************************************
 *   This program is free software; you can redistribute it and/or modify  *
 *   it under the terms of the GNU General Public License as published by  *
 *   the Free Software Foundation; either version 3 of the License, or     *
 *   (at your option) any later version.                                   *
 ***************************************************************************/

/*!
 * \file SimulatedRobotControl.c
 *
 * \author Niceto R. Luque
 * \author Richard R. Carrillo
 * \date 7 of November 2013
 * In this file the main robot-control loop is implemented.
 */

#include <iostream>

using namespace std;


#if defined(_DEBUG) && (defined(_WIN32) || defined(_WIN64))
#   define _CRTDBG_MAP_ALLOC
#   include <crtdbg.h> // To check for memory leaks
#endif

#if defined(__APPLE_CC__)
  // MAC OS X includes
#	define REAL_TIME_OSX
#elif defined (__linux__)
  // Linux includes
#	define REAL_TIME_LINUX
#elif (defined(_WIN32) || defined(_WIN64))
#	define REAL_TIME_WINNT
#else
#	error "Unsupported platform"
#endif

#if defined(REAL_TIME_OSX)
#	include <mach/mach.h>
#	include <mach/mach_time.h>
#	include <CoreServices/CoreServices.h>
#	include <unistd.h>
#elif defined(REAL_TIME_LINUX)
#	include <time.h>
#elif defined(REAL_TIME_WINNT)
#	include <windows.h>
#endif

#include <stdio.h>

#include "../include/interface/C_interface_for_robot_control.h"
//ROBOT//
#include "../include/arm_robot_simulator/ArmRobotSimulation.h"

#include "../include/openmp/openmp.h"


// Neural-network simulation files
#define NET_FILE "NetWithGolgiStep3_1.net"//"NetDistributedPlasticity2dcnCos.net"// Neural-network definition file used by EDLUT
#define INPUT_WEIGHT_FILE "WeightWithGolgiStep3_1.net"//"WeightNetDistributedPlasticity2dcnFinal.net"// Neural-network input weight file used by EDLUT
#define OUTPUT_WEIGHT_FILE "OutputWeight.dat" // Neural-network output weight file used by EDLUT
#define WEIGHT_SAVE_PERIOD 0 // The weights will be saved each period (in seconds) (0=weights are not saved periodically)
//#define INPUT_ACTIVITY_FILE  "ActivityClosingLoop2DCNGR25trajectory1seg_during5000seg.dat"//"Activity2dcnALLLearnings25trajectory1seg_during2500seg.dat" //Optional input activity file 
#define INPUT_ACTIVITY_FILE 0
#define OUTPUT_ACTIVITY_FILE "OutputActivity.dat" // Output activity file used to register the neural network activity
#define LOG_FILE "vars.dat"  // Log file used to register the simulation variables
#define REAL_TIME_NEURAL_SIM 0 // EDLUT's simulation mode (0=No real-time neural network simulation 1=For real robot control)
#define FIRST_REAL_TIME_RESTRICTION 0.7
#define SECOND_REAL_TIME_RESTRICTION 0.8
#define THIRD_REAL_TIME_RESTRICTION 0.9

#define MAX_SIMULATION_DELAY 0.05f

#define NUMBER_OF_OPENMP_QUEUES 1
#define NUMBER_OF_OPENMP_THREADS 1

// external error
//#define ERROR_AMP 1 // Amplitude of the injected error
//#define NUM_REP 1 // Number of repetition of the exponential shape along the Trajectory Time
//#define ROBOT_JOINT_ERROR_NORM 160 // proportional error {160}

#define TRAJ_POS_AMP 0.1 // Amplitude of the desired robot's trajectory
#define TRAJECTORY_TIME 1 // Simulation time in seconds required to execute the desired trajectory once
#define MAX_TRAJ_EXECUTIONS 10 // Maximum number of trajectories repetitions that will be executed by the robot
#define ERROR_DELAY_TIME 0.1 // Delay after calculating the error vars


// Robot's dynamics files
#define ROBOT_VAR_FILE_NAME "MANIPULATORS.mat" // MATLAB's file containing the robot's specifications.
#define ROBOT_BASE_VAR_NAME "RRedKuKa" // Variable name in ROBOT_VAR_FILE_NAME which contains the base robot specifications.
#define ROBOT_SIMUL_VAR_NAME "RRedKuKadet6" // Variable name in ROBOT_VAR_FILE_NAME which contains the simulated robot specifications.

const double ROBOT_GRAVITY[3]={0, 0, 9.81}; // Earth's standard acceleration due to gravity [Gx Gy Gz]
const double ROBOT_EXTERNAL_FORCE[6]={0, 0, 0, 0, 0, 0}; // External force on manipulator tip [Fx Fy Fz MOMENTUMx MOMENTUMy MOMENTUMz]


///////////////////////////// MAIN LOOP //////////////////////////

int main(int ac, char *av[])
{

      
	int errorn;
	long total_output_spks; 
	double input_traject_vars[NUM_JOINTS*3]; // Desired trajectory position, velocity and acceletarion
	double robot_state_vars[NUM_JOINTS*3]; // Actual robot position, velocity and acceleration
	double robot_inv_dyn_torque[NUM_JOINTS]; // Robot's inverse dynamics torque
	double total_torque[NUM_JOINTS]; // Total torque applied to the robot
	double robot_error_vars[NUM_JOINTS]; // Joint error (PD correction)
   
	double cerebellar_output_vars[NUM_OUTPUT_VARS]={0.0}; // Corrective cerebellar output torque
	double cerebellar_error_vars[NUM_JOINTS*3]={0.0}; // error corrective cerebellar output torque
	double cerebellar_learning_vars[NUM_OUTPUT_VARS]={0.0}; // Error-related learning signals
  
   
	// delayed Error-related learning signals

	double *delayed_cerebellar_learning_vars;

   
	// Robot's dynamics variables
	mxArray *robot_inv_dyn_object=NULL, *robot_dir_dyn_object=NULL;
	struct integrator_buffers num_integration_buffers; // Integration buffers used to simulate the robot
   
	// Simul variables
	Simulation *neural_sim;
   
	// Robot variables
	int n_robot_joints;
   
	// Time variables
	double sim_time,cur_traject_time;
	float slot_elapsed_time,sim_elapsed_time;
	int n_traj_exec;
   
	// Delays
	struct delay cerebellar_delay;
	struct delay cerebellar_learning_delay;
	struct delay cerebellar_delay_normalized;
   
	// Variable for logging the simulation state variables
	struct log var_log;
 

#if defined(REAL_TIME_WINNT)
	// Variables for consumed-CPU-time measurement
	LARGE_INTEGER startt,endt,freq;

#elif defined(REAL_TIME_OSX)
	uint64_t startt, endt, elapsed;
	static mach_timebase_info_data_t freq;
#elif defined(REAL_TIME_LINUX)
	// Calculate time taken by a request - Link with real-time library -lrt
	struct timespec startt, endt, freq;
#endif

#if defined(_DEBUG) && (defined(_WIN32) || defined(_WIN64))
	//   _CrtMemState state0;
	_CrtSetReportMode(_CRT_WARN, _CRTDBG_MODE_FILE);
	_CrtSetReportFile(_CRT_WARN, _CRTDBG_FILE_STDERR);
#endif

#if defined(REAL_TIME_WINNT)
	if(!QueryPerformanceFrequency(&freq))
		puts("QueryPerformanceFrequency failed");
#elif defined (REAL_TIME_LINUX)
	if(clock_getres(CLOCK_REALTIME, &freq))
		puts("clock_getres failed");
#elif defined (REAL_TIME_OSX)
	// If this is the first time we've run, get the timebase.
	// We can use denom == 0 to indicate that sTimebaseInfo is
	// uninitialised because it makes no sense to have a zero
	// denominator is a fraction.
	if (freq.denom == 0 ) {
		(void) mach_timebase_info(&freq);
	}
#endif

	// Load Matlab robot objects from Matlab files
	if(!(errorn=load_robot(ROBOT_VAR_FILE_NAME,ROBOT_BASE_VAR_NAME,&n_robot_joints,&robot_inv_dyn_object)) && \
		!(errorn=load_robot(ROBOT_VAR_FILE_NAME,ROBOT_SIMUL_VAR_NAME,NULL,&robot_dir_dyn_object)))
    	{
		if(!(errorn=allocate_integration_buffers(&num_integration_buffers,n_robot_joints))) // Initialize the buffers for numerical integration using the initial desired robot's state
		{
			// Initialize variable log
			if(!(errorn=create_log(&var_log, MAX_TRAJ_EXECUTIONS, TRAJECTORY_TIME)))
			{
				// Initialize EDLUT and load neural network files
				neural_sim=create_neural_simulation(NET_FILE, INPUT_WEIGHT_FILE, INPUT_ACTIVITY_FILE, OUTPUT_WEIGHT_FILE, OUTPUT_ACTIVITY_FILE, WEIGHT_SAVE_PERIOD, NUMBER_OF_OPENMP_QUEUES, NUMBER_OF_OPENMP_THREADS);
				if(neural_sim)
				{
					total_output_spks=0L;
					puts("Simulating...");
					sim_elapsed_time=0.0;
					errorn=0;
//    _CrtMemCheckpoint(&state0);
					bool real_time_neural_simulation=false;
					if(REAL_TIME_NEURAL_SIM==1){
						#ifdef _OPENMP 
							omp_set_nested(true);
							real_time_neural_simulation=true;
							cout<<"\nFixed REAL TIME SIMULATION option\n"<<endl;
							init_real_time_restriction(neural_sim, SIM_SLOT_LENGTH, MAX_SIMULATION_DELAY, FIRST_REAL_TIME_RESTRICTION, SECOND_REAL_TIME_RESTRICTION, THIRD_REAL_TIME_RESTRICTION);
						#else
							cout<<"\nREAL TIME SIMULATION option is not available due to the openMP support is disabled\n"<<endl;
						#endif
					}
					#pragma omp parallel if(real_time_neural_simulation) num_threads(2) 
					{
						if(omp_get_thread_num()==1){
							start_real_time_restriction(neural_sim);
						}else{
							#pragma omp parallel if(NumberOfOpenMPThreads>1) default(shared) private( n_traj_exec, sim_time, cur_traject_time)
							{
								//Select the correspondent device.
								neural_sim->SelectGPU(omp_get_thread_num());


								if(omp_get_thread_num()>0){
									for(n_traj_exec=0;n_traj_exec<MAX_TRAJ_EXECUTIONS && !errorn;n_traj_exec++){
										cur_traject_time=0.0;
										do
										{
											// control loop iteration starts
											sim_time=(double)n_traj_exec*TRAJECTORY_TIME + cur_traject_time; // Calculate absolute simulation time
			  					
											errorn=run_neural_simulation_slot(neural_sim, sim_time+SIM_SLOT_LENGTH); // Simulation the neural network during a time slot

											cur_traject_time+=SIM_SLOT_LENGTH;
										}
										while(cur_traject_time<TRAJECTORY_TIME-(SIM_SLOT_LENGTH/2.0) && !errorn); // we add -(SIM_SLOT_LENGTH/2.0) because of floating-point-type codification problems
            						} 
								}else{
									for(n_traj_exec=0;n_traj_exec<MAX_TRAJ_EXECUTIONS && !errorn;n_traj_exec++){
										calculate_input_trajectory(robot_state_vars, TRAJ_POS_AMP, 0.0); // Initialize simulated robot's actual state from the desired state (input trajectory) (position, velocity and acceleration)
										initialize_integration_buffers(robot_state_vars,&num_integration_buffers,n_robot_joints); // For the robot's direct                 
										init_delay(&cerebellar_learning_delay, ERROR_DELAY_TIME);
				 
										//////////////////////////////REVISAR ESTE IF///////////////////////////////////////
										if(INPUT_ACTIVITY_FILE==0){
											reset_neural_simulation(neural_sim); // after each trajectory execution the network simulation state must be reset (pending activity events are discarded)
////////////////////////////////////////////////////////							
	double time=((double)n_traj_exec)*TRAJECTORY_TIME;
	calculate_input_activity_for_one_trajectory(neural_sim, time);
////////////////////////////////////////////////////////////
										} 
					 									 
										cur_traject_time=0.0;
					 					
										if(REAL_TIME_NEURAL_SIM==1){
											reset_real_time_restriction(neural_sim);
										}
										do
										{
											int n_joint;
											if(REAL_TIME_NEURAL_SIM==1){
												next_step_real_time_restriction(neural_sim);
											}
											#if defined(REAL_TIME_WINNT)
        										QueryPerformanceCounter(&startt);
											#elif defined(REAL_TIME_LINUX)
        										clock_gettime(CLOCK_REALTIME, &startt);
											#elif defined(REAL_TIME_OSX)
        										startt = mach_absolute_time();
											#endif

											// control loop iteration starts                  
					
											calculate_input_trajectory(input_traject_vars, TRAJ_POS_AMP, cur_traject_time); // Calculate desired input trajectory
											sim_time=(double)n_traj_exec*TRAJECTORY_TIME + cur_traject_time; // Calculate absolute simulation time
				 
											compute_robot_inv_dynamics(robot_inv_dyn_object,input_traject_vars,ROBOT_EXTERNAL_FORCE,ROBOT_GRAVITY,robot_inv_dyn_torque); // Calculate crude inverse dynamics of the base robot. They constitude the base robot's input torque
                    
					 
											for(n_joint=0;n_joint<NUM_JOINTS;n_joint++) // Calculate total torque from forward controller (cerebellum) torque plus base controller torque
												total_torque[n_joint]=robot_inv_dyn_torque[n_joint]+(cerebellar_output_vars[n_joint*2]-cerebellar_output_vars[n_joint*2+1]);
                    
											compute_robot_dir_dynamics(robot_dir_dyn_object,robot_state_vars,total_torque,robot_state_vars,&num_integration_buffers,ROBOT_EXTERNAL_FORCE,ROBOT_GRAVITY,SIM_SLOT_LENGTH); // Simulate the robot (direct dynamics).
											calculate_error_signals(input_traject_vars, robot_state_vars, robot_error_vars); // Calculated robot's performed error
											calculate_learning_signals(robot_error_vars, cerebellar_output_vars, cerebellar_learning_vars); // Calculate learning signal from the calculated error			
					
											delayed_cerebellar_learning_vars=delay_line(&cerebellar_learning_delay,cerebellar_learning_vars);
											generate_learning_activity(neural_sim, sim_time,delayed_cerebellar_learning_vars);  
					  					
											errorn=run_neural_simulation_slot(neural_sim, sim_time+SIM_SLOT_LENGTH); // Simulation the neural network during a time slot
                     
											total_output_spks+=(long)compute_output_activity(neural_sim, cerebellar_output_vars); // Translates cerebellum output activity into analog output variables (corrective torques)
                     
											// control loop iteration ends

#if defined(REAL_TIME_WINNT)
											QueryPerformanceCounter(&endt); // measures time
											slot_elapsed_time=(endt.QuadPart-startt.QuadPart)/(float)freq.QuadPart; // to be logged
#elif defined(REAL_TIME_LINUX)
											clock_gettime(CLOCK_REALTIME, &endt);
											// Calculate time it took
											slot_elapsed_time = (endt.tv_sec-startt.tv_sec ) + (endt.tv_nsec-startt.tv_nsec )/float(1e9);
#elif defined(REAL_TIME_OSX)
											// Stop the clock.
											endt = mach_absolute_time();
											// Calculate the duration.
											elapsed = endt - startt;
											slot_elapsed_time = 1e-9 * elapsed * freq.numer / freq.denom;
#endif
                    
                     
											sim_elapsed_time+=slot_elapsed_time;
											log_vars(&var_log, sim_time, input_traject_vars, robot_state_vars, robot_inv_dyn_torque, cerebellar_output_vars, cerebellar_learning_vars, robot_error_vars, slot_elapsed_time,get_neural_simulation_spike_counter_for_each_slot_time()); // Store vars into RAM
											cur_traject_time+=SIM_SLOT_LENGTH;
					 					 
										}
										while(cur_traject_time<TRAJECTORY_TIME-(SIM_SLOT_LENGTH/2.0) && !errorn); // we add -(SIM_SLOT_LENGTH/2.0) because of floating-point-type codification problems
					
									} 
								}
								if(real_time_neural_simulation){
									stop_real_time_restriction(neural_sim);
								}
							}
						}
					}
//     reset_neural_simulation(neural_sim);
//     _CrtMemDumpAllObjectsSince(&state0);
					if(errorn)
						printf("Error %i performing neural network simulation\n",errorn);
					printf("Total neural-network output spikes: %li\n",total_output_spks);
					printf("Total number of neural updates: %Ld\n",get_neural_simulation_event_counter(neural_sim));
					printf("Mean number of neural-network spikes in heap: %f\n",get_accumulated_heap_occupancy_counter(neural_sim)/(double)get_neural_simulation_event_counter(neural_sim));

					for(int i=0; i<neural_sim->GetNumberOfQueues(); i++){
						cout << "Thread "<<i<<"--> Number of updates: " << neural_sim->GetSimulationUpdates(i) << endl; /*asdfgf*/
						cout << "Thread "<<i<<"--> Number of InternalSpike: " << neural_sim->GetTotalSpikeCounter(i) << endl; /*asdfgf*/
						cout << "Thread "<<i<<"--> Number of Propagated Spikes and Events: " << neural_sim->GetTotalPropagateCounter(i)<<", "<< neural_sim->GetTotalPropagateEventCounter(i)<< endl; /*asdfgf*/
						cout << "Thread "<<i<<"--> Mean number of spikes in heap: " << neural_sim->GetHeapAcumSize(i)/(float)neural_sim->GetSimulationUpdates(i) << endl; /*asdfgf*/
					}
					cout << "Total InternalSpike: " << neural_sim->GetTotalSpikeCounter()<<endl; 
					cout << "Total Propagated Spikes and Events: " << neural_sim->GetTotalPropagateCounter()<<", "<< neural_sim->GetTotalPropagateEventCounter()<<endl;

#if defined(REAL_TIME_WINNT)
					printf("Total elapsed time: %fs (time resolution: %fus)\n",sim_elapsed_time,1.0e6/freq.QuadPart);
#elif defined(REAL_TIME_LINUX)
					printf("Total elapsed time: %fs (time resolution: %fus)\n",sim_elapsed_time,freq.tv_sec*1.0e6+freq.tv_nsec/float(1e3));
#elif defined(REAL_TIME_OSX)
					printf("Total elapsed time: %fs (time resolution: %fus)\n",sim_elapsed_time,1e-3*freq.numer/freq.denom);
#endif

					save_neural_weights(neural_sim);
					finish_neural_simulation(neural_sim);
				}
				else
				{
					errorn=10000;
					printf("Error initializing neural network simulation\n");
				}              
				puts("Saving log file");
				errorn=save_and_finish_log(&var_log, LOG_FILE); // Store logged vars in disk
				if(errorn)
					printf("Error %i while saving log file\n",errorn);
			}
			else
			{
				errorn*=1000;
				printf("Error allocating memory for the log of the simulation variables\n");
			}         
       
			free_robot(robot_inv_dyn_object);
			free_robot(robot_dir_dyn_object);
		}
		else
		{
			errorn*=10;
			printf("Error loading the robot object from file: %s\n",ROBOT_VAR_FILE_NAME);
		}	 
		if(!errorn)
			puts("OK");
		else
			printf("Error: %i\n",errorn);
#if defined(_DEBUG) && (defined(_WIN32) || defined(_WIN64))
		_CrtDumpMemoryLeaks();
#endif
		
	}else{
		printf("Error loading robot: %i\n",errorn);
	}
	return(errorn);
}
